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Updated: Jan 11, 2026
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Development and validation of a GP73-based predictive model for the diagnosis of early-stage hepatocellular carcinoma
Shan Ji1, Yumeng Liu1, Yuhua Li1
1Department of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Background:
Hepatocellular carcinoma (HCC) has become a major global public health concern due to its high incidence, high mortality, and frequent diagnosis at advanced stages. Currently available biomarkers, such as alpha-fetoprotein (AFP), have limited sensitivity and specificity for early-stage HCC, making early screening a significant challenge. Therefore, identifying novel and complementary biomarkers is essential to improve early detection rates and patient prognosis. Golgi protein-73 (GP73) is a secreted glycoprotein that is highly expressed in various malignancies and can be released into the bloodstream. Herein, we evaluated its potential value for early diagnosis of HCC.
Methods:
This retrospective cross-sectional study aimed to evaluate the diagnostic value of serum GP73 for early-stage HCC. A total of 401 patients were included in the study, comprising 63 patients with early-stage HCC and 338 patients with liver cirrhosis. Diagnoses of HCC and cirrhosis were confirmed based on a combination of imaging, pathological findings, and clinical guidelines. Serum levels of GP73 and AFP were measured using enzyme-linked immunosorbent assay (ELISA), and other clinical variables such as age and prothrombin time (PT) were obtained from medical records.
Results:
The area under the curve (AUC) for AFP and GP73 alone in diagnosing early-stage HCC was 0.769 [95% confidence interval (CI): 0.700-0.838] and 0.627 (95% CI: 0.539-0.716), respectively. After adjustment in multivariate analysis, GP73 remained an independent diagnostic factor (P<0.05). The developed nomogram achieved corrected C-indexes of 0.812 and 0.918 in the training and validation cohorts, respectively. Among patients infected with hepatitis B virus (HBV), the nomogram demonstrated AUCs of 0.836 in the training cohort and 0.900 in the validation cohort, indicating good discriminative ability. Baseline characteristics were comparable between the groups.
Conclusions:
The nomogram model proposed in this study, which integrates GP73, AFP, age, and PT, may serve as a simple, intuitive, and customizable clinical tool for identifying early-stage HCC among patients with liver cirrhosis. The model exhibits high diagnostic performance, especially in HBV-related populations, and shows promising potential for clinical application.
